def get_trades(asset_symbol, timedelta=datetime.timedelta(minutes=10), start=False, end=False, time_offset=datetime.timedelta(hours=5)):
if not start or not end: # create default start and end times if not given
end = datetime.datetime.now() + time_offset
start = end - timedelta
else:
start += time_offset
end += time_offset
# create unix versions of the start and end times
end_unix = int(time.mktime(end.timetuple()))
start_unix = int(time.mktime(start.timetuple()))
# get raw trade data from poloniex
trades_data = p.returnMarketTradeHistory('BTC_'+asset_symbol,start=start_unix,end=end_unix)
if len(trades_data) > 0: # create a data frame with all the trades
trades_df = pd.DataFrame(trades_data)
trades_df[['amount','rate','total']] = trades_df[['amount','rate','total']].astype(float)
trades_df['date'] = pd.to_datetime(trades_df.date)
trades_df['symbol'] = asset_symbol
return trades_df
else:
return false
def get_cmc_markets(asset_name):
# get the markets data from coinmarketcap
base_url = 'http://coinmarketcap.com/currencies/'
web_page = BeautifulSoup(requests.get(base_url+asset_name).text)
markets_section = web_page.select('div#markets')[0]
market_table_rows = markets_section.find('table').find_all('tr')[1:]
markets = [] # store each market instance in an array
for market in market_table_rows:
markets.append([td.text for td in market.find_all('td')])
# create data frame with each markets data
columns = ['ranking','exchange','currency_pair','volume', 'price', 'market_share','update_time']
markets_df = pd.DataFrame(markets, columns=columns)
markets_df.index = markets_df.ranking.values
markets_df['volume'] = markets_df.volume.apply(lambda v: int(''.join([c for c in v if c.isdigit()])))
markets_df['price'] = markets_df.price.apply(lambda p: float(p.split(' ')[1]))
markets_df['market_share'] = markets_df.market_share.apply(lambda s: float(s.split(' ')[0]) / 100.)
markets_df['recently_updated'] = markets_df.update_time.apply(lambda u: 1 if u == 'Recently' else 0)
markets_df = markets_df.drop(['ranking', 'update_time'],1)
return markets_df